Quantum intraday alerting based on radio access network outlier analysis

ABSTRACT

Determining RAN performance outlier information is disclosed. In an aspect, RAN data for a current condition can be analyzed in view of historical RAN data to determine if the current condition in atypical and represents an outlier condition. In an aspect, historically similar environments between the historical and current RAN data can allow the analysis to have increased relevance. The disclosure can employ a binary array to facilitate determining performance outlier information. In an aspect, the present disclosure can be performed in a distributed computing environment and is scalable to manage large data volumes. Performance outlier information can be employed in alerts, scheduling repair, planning hardware or software upgrades, tracking rollout of new features, etc.

RELATED APPLICATION

The subject application is a divisional application of, and claimspriority to, U.S. patent application Ser. No. 15/019,952 (now U.S. Pat.No. 10,440,594), filed 9 Feb. 2016, and entitled “QUANTUM INTRADAYALERTING BASED ON RADIO ACCESS NETWORK OUTLIER ANALYSIS,” the entiretyof which application is hereby incorporated by reference herein.

TECHNICAL FIELD

The disclosed subject matter relates to an analysis of radio accessnetwork (RAN) performance, e.g., for determining RAN performance outlierconditions.

BACKGROUND

By way of brief background, conventional analysis of radio accessnetwork (RAN) conditions is often based simply on manipulations ofcurrent RAN performance values. These conventional techniques generallyignore historically similar performance. Moreover, conventionaltechniques can be considered rudimentary, for example, flagging a RANbased on a current performance indicator exceeding a threshold value. Assuch, conventional analysis of RAN performance can be improved upon,which can result in, for example, a reduction in false alerts to aunderperforming RAN that can, in turn, reduce costs associated withresponding to a false alert.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is an illustration of an example system that facilitatesinitiating an alert based on performance outlier analysis in accordancewith aspects of the subject disclosure.

FIG. 2 is a depiction of an example system that facilitates respondingto a determined RAN performance condition based on performance outlieranalysis in accordance with aspects of the subject disclosure.

FIG. 3 illustrates an example system that facilitates access toperformance outlier information based on performance outlier analysis ofcurrent and historical RAN information in accordance with aspects of thesubject disclosure.

FIG. 4 illustrates an example system that facilitates access toperformance outlier information based on performance outlier analysis ofcurrent RAN information and historical RAN information received from adata store in accordance with aspects of the subject disclosure.

FIG. 5 illustrates an example system depicting enabling access toperformance outlier information determined via a distributed computingenvironment in accordance with aspects of the subject disclosure.

FIG. 6 illustrates example RAN performance data, an example binaryarray, example determined performance outlier information, and anexample data plot corresponding related thereto, in accordance withaspects of the subject disclosure.

FIG. 7 illustrates example RAN performance data corresponding to anexample performance outlier condition, and additional exampleperformance outlier information than can be determined in accordancewith aspects of the subject disclosure.

FIG. 8 illustrates an example method facilitating initiating an alertbased on performance outlier analysis in accordance with aspects of thesubject disclosure.

FIG. 9 depicts an example method facilitating determining performanceoutlier information based on performance outlier analysis of current andhistorical RAN information in accordance with aspects of the subjectdisclosure.

FIG. 10 illustrates an example method facilitating determiningperformance outlier information via a distributed computing environmentin accordance with aspects of the subject disclosure.

FIG. 11 depicts an example schematic block diagram of a computingenvironment with which the disclosed subject matter can interact.

FIG. 12 illustrates an example block diagram of a computing systemoperable to execute the disclosed systems and methods in accordance withan embodiment.

DETAILED DESCRIPTION

The subject disclosure is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the subject disclosure. It may be evident, however,that the subject disclosure may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to facilitate describing the subjectdisclosure.

Conventional analysis of radio access network (RAN) conditions is oftenbased simply on manipulations of RAN performance values reflecting onlycurrent RAN conditions. These conventional techniques generally ignoreRAN performance in historically similar environments. Moreover,conventional techniques can generally be considered rudimentary. As anexample, a RAN can be flagged as underperforming based merely on acurrent performance indicator exceeding a fixed threshold value, whichcan result in, for example, a false alert associated with a responsecost that might be otherwise avoidable. The alert can be false, forexample, where the performance is characteristic of the RAN device in ahistorically similar environment.

Historically similar environments can be related to time, location,political/cultural/sporting events, weather patterns/conditions,emergencies/disasters, hardware or software malfunctions or errors of acomputer/device associated with a RAN, etc. As an example, ahistorically similar environment can be associated with inflow rush hourtraffic, e.g., RAN data for the last 12 Mondays from 8 am-9 am, etc. Afurther example historically similar environment can be related to lunchtimes at a large employer, e.g., RAN data from noon-1 pm for the last 15business days (e.g., excluding Sat, Sun), etc. As a still furtherexample of a historically similar environment, RAN usage during asporting event or for a type of sporting event can be identified, e.g.,RAN data for a 15 minute interval immediately prior to an professionalfootball game start time for each game in the season, which can bedistinct from a historically similar environment for a 15 minuteinterval immediately prior to a college or high school football game, asoccer game, a hockey game, etc., and which can further be distincthistorically similar environments between afternoon games, eveninggames, etc. It is to be noted that use of RAN data associated with ahistorically similar environment can allow RAN performance analysis toidentify fewer false outliers than an analysis that ignores historicaldata, and more especially that ignores historically similar environmentdata, because a RAN is presumed to operate similarly for eachhistorically similar environment. As such, where, for example, a RANdevice demonstrates higher failure rates during high call volumesassociated with seasonal heavy snowfall, it can be expected that acurrent high fail rate for the RAN device on a sunny dry day is morelikely to be an actual failure condition than if the higher fail ratewere occurring during heavy snow. Nearly any selection criteria and datacan be employed in selecting historical RAN data associated with ahistorically similar environment to current RAN key performanceindicators (KPIs) being analyzed for outlier behavior, and all suchselection criteria, data, and techniques are within the scope of theinstant disclosure despite not being enumerated for the sake of clarityand brevity. Moreover, the selection of historically relevant RAN data,e.g., historical RAN data associated with a historically similarenvironment, can be simple, e.g., for a particular time window on adaily period, etc., to more complex, e.g., at one minute intervalsduring a non-commercial segments of Friday night first run showings apopular nightly television program, etc. Construction of selectionrules/criteria, and/or exclusion rules criteria, can be consideredengineering decisions, all of which are within the scope of the presentdisclosure.

RAN performance outlier analysis can relate to any measurable valuerelated to RAN devices. Of note, a RAN can be a macro level RAN, e.g., acellular network RAN, or another RAN, e.g., a femtocell, picocell,corporate wireless LAN, etc. In an aspect, performance outlier analysiscan relate to KPIs. These can facilitate determinations about RANperformance. As examples, RAN information can comprise metrics such astotal calls, failed calls, dropped calls, failure rates, handover drops,throughput rates, latency, etc. Outliers can indicate changes in theoperation of a RAN. In an aspect, a change in the operation of a RAN canresult from changes in the RAN environment, such as those disclosedherein with regard to historically similar RAN environments, such that,the change affects how a user might experience service via a RAN. As anexample, a RAN device can manage a moderate level of wireless linkswithout notable failure counts, however, under higher loads the RANdevice can demonstrate worsening performance, e.g., a RAN device canoperate satisfactorily early in the morning, then operatenon-satisfactorily at rush hour, then return to satisfactory operationafter rush hour. This example change in performance can be due to aninherent limitation of a properly operating RAN device, can be due tounusual demands placed on the RAN device during a particularly busy rushhour, can be due to equipment failure of the RAN device, can be due tonon-optimized RAN device parameters, can be due to a software glitch inthe RAN or affiliated device, etc.

Detection of the change from satisfactory to non-satisfactory (or theconverse) can be important in providing a valuable consumer experienceon a wireless network by indicating needed upgrades, repairs, softwarepatching, deployment of additional RAN devices in congested areas, etc.However, conventional techniques can flag an otherwise normallyoperating RAN device at every rush hour, where the increase in trafficdecreases RAN device performance for example. This can divert resourcesfrom addressing other actually non-normally operating RAN devices. Theinstant disclosure enables analysis that considers the typicalperformance of a normally operating RAN device in historically similarenvironments, e.g., it can consider the typical performance of a subjectRAN device in a current rush hour to how it performed in previous rushhours to help determine if the lack of performance is unusual for thatRAN device in that environment. This can allow a RAN that simply underperforms regularly in a particular environment to be detected asunderperforming but also identified as an outlier when theunderperformance is unusual even for that device in said environment.This can, for example, allow resources to be more effectively deployedto devices that are both underperformers and outliers before beingdirected to underperformers that are not outliers. As another example, aRAN device can drop more calls with increased load, such as from largenumbers of people attending a football game. Where the football team ishaving a great season, attendance at the game can be even higher thannormal, this can cause even greater numbers of dropped calls for the RANdevice. This unusual increase in dropped calls, related to a winningfootball team, can be detected via the instant disclosure as an outlier,e.g., a statistical change in the performance of the RAN device incomparison to the historic performance of the same device during otherfootball games. This can allow the network operator to address thisissue, perhaps with greater urgency than they might have were thefootball team having a normal, rather than excellent, year.

In an aspect, the amount of data to be analyzed for more than a few RANdevices can be noteworthy. To this end, aspects of the presentlydisclosed subject matter can be performed in a distributed computingenvironment, cluster computing environment, etc. As an example, thepresent disclosure can be executed in an Apache™ Spark™ cluster, etc.,which can allow spreading computational work across a plurality of datanodes and/or clusters, with data redundancy, scalability, and in-processmemory processing on low-cost commodity hardware. This aspect can beincreasingly important as internet of things (IOT) devices become moreubiquitous and communicate wirelessly with macro-RANs, femtocells,picocells, and other wireless network access systems. In someembodiments, RAN devices can compute current KPI values, store historicKPI values, associate those values to historic environment values,select which historic KPI values are relevant, etc., and enable accessto this information to facilitate performance outlier analysis in adistributed and/or centralized manner. As an example, a RAN with thistechnology can perform outlier analysis and request attention when anoutlier is detected, can provide access to the outlier analysis to allowa centralized system to determine how to respond to an outlier, cansource KPI and relevant historic KPI information to a centralizedanalysis component, etc.

Outlier analysis can determine a relationship between a first KPI andone or more historic KPIs. In some embodiments, the KPIs, both first andhistoric, can be combined with a binary array to allow for determinationof a correlation coefficient. In certain embodiments, the correlationcoefficient can be a Pearson correlation, also known as the PearsonProduct Moment Correlation (PPMC) and frequently denoted as ‘r’ (rho).In PPMC, the closer rho is to +/−1, the stronger the correlation and thecloser to zero the lower the correlation. As such, the use of the binaryarray can allow for KPI values satisfying a rule related to a triggervalue to be used as ‘normal’ and other KPI values not satisfying therule to be treated as ‘non-normal’. This distinction can enable the KPIsto be used in determining metrics related to a RAN device. As anexample, a sudden uptick in failures, such going from 5 per 1000 to 35per 1000, can present as a high rho value, such as 0.95+, this can allowolder and lower failure rates (5 per 1000) to be used to determine abaseline failure rate that can be leveraged to determine an measurablechange in failures associated with the outlier, such as +30 failures per1000 over baseline for a historically similar environment.

To the accomplishment of the foregoing and related ends, the disclosedsubject matter, then, comprises one or more of the features hereinaftermore fully described. The following description and the annexed drawingsset forth in detail certain illustrative aspects of the subject matter.However, these aspects are indicative of but a few of the various waysin which the principles of the subject matter can be employed. Otheraspects, advantages and novel features of the disclosed subject matterwill become apparent from the following detailed description whenconsidered in conjunction with the provided drawings.

FIG. 1 is an illustration of a system 100, which facilitates initiatingan alert based on performance outlier analysis in accordance withaspects of the subject disclosure. System 100 can comprise performanceoutlier analysis component (POAC) 120. POAC 120 can receive radio accessnetwork (RAN) data 102. RAN data 102 can comprise current informationrelated to a RAN device. RAN data 102 can further comprise historicinformation related to a RAN device. In some embodiments, RAN data 102can comprise key performance indicators (KPIs) related to operation of aRAN device. As examples, RAN data 102 an comprise information related toevents such as establishing and/or maintaining a call/data session,handoffs, dropped calls/data sessions, reestablished calls/datasessions, throughput, jitter, bandwidth, device identification, RANdevice environment, e.g., time, date, weather, events, network traffic,vehicular or pedestrian traffic, software versions, operational uptime,repair information, hardware characteristics including temperature,versions, voltage, current, power, etc., position, rankings,self-organizing network information, network routing information, etc.Nearly any data associated with the operation of a RAN device or theenvironment and characteristics of the RAN device or associated networkcan be part of RAN data 102 and can be current or historical in nature.

POAC 120 can determine, based on RAN data 102, performance aspects of aRAN device or associated network, including information related toatypical or anomalistic behavior, e.g., a performance outlier. Aperformance outlier can be related to RAN device related values that areoutside of a determined or specified normal operation parameter. In anaspect, a performance outlier can be directly identified where a valueis determined to satisfy a rule related to the normal value ascribed tothe associated performance parameter, for example, a higher than normaloperating temperature for a processor of a RAN device can be determinedto be a performance outlier. In another aspect, a performance outliercan be determined based on analysis of RAN data 102 to determineatypical behavior, e.g., a high level of processor usage can be analyzedto determine that a software feature is stuck in a loop and be flaggedas a performance outlier. This analytical aspect can be expanded todetermine outliers based on historical performance. As an example, wherea RAN device located in a grassy flat region experiences less than 0.1%dropped call rate on average, based on historical data, a KPI indicatinga 0.9% drop call rate can be determined to be an outlier. However, wherea similar RAN device is located in a hilly and densely treed area, witha historical average of, for example, 0.87% dropped call rate the same0.9% value can be determined to be within normal operating range. In anaspect, the instant disclosure can enable determination of normalbehavior for a RAN device with regard to historically similarenvironments. As an example, the example RAN device with a historicalaverage of 0.87% can receive a hardware upgrade that can decrease thenumber of dropped calls. The historical average will therefore decreasefrom 0.87% over time, however, where the instant disclosure is applied,dropped call rates, for example, from before the hardware upgrade can bestatistically compensated for such that a current 0.9% drop rate can bedetermined to be an outlier in view of lower drop rates since thehardware upgrade.

POAC 120 can be employed to automate outlier analysis for RAN devices.This can support scalable application of outlier analysis to a pluralityof RAN devices, e.g., the large number of RAN device associated with awireless carrier access network, ad hoc additions of femtocells, etc.Moreover, POAC 120 can facilitate analysis of large amounts of data,such as can be received from a wireless access network at the carrier orenterprise level. In some embodiments, massive amounts of data can bequickly processed in an efficient manner by employing distributedcomputing paradigms, for example, on a Linux cluster with Spark datanodes.

POAC 120 can facilitate access to performance outlier information 190.Performance outlier information 190 can comprise information related toanalysis of RAN data 102 via POAC 120. In some embodiments, performanceoutlier information 190 can comprise identification of a RAN devicedetermined to have an outlying parameter or condition. As an example,performance outlier information 190 can comprise a list of RAN devicesdetermined to have outlying operation values to aid in directingresources to correcting the outlying operation, e.g., repairs, upgrades,resets, etc. Performance outlier information 190 can comprise, in someembodiments can further comprise other information related to a RANdevice or analysis of RAN data 102. In a related embodiment, performanceoutlier information 190 can comprise information related to ahistorically similar environment for a RAN associated with an outliervalue, identification of an event associated with evolution of anoutlier value, e.g., information indicating the rapid onset of anoutlier condition, indicating that the value has only been slowlydegrading until it reaches an outlier condition, etc., an alert, a flag,a request for response, etc. As an example, performance outlierinformation 190 can include an alarm trigger, a determined outliervalue, identification of the RAN device associated with the outliervalue, and information indicating that a political rally occurred withinthe coverage area of the identified RAN device proximate in time to thedetermined outlier event. This example illustrates that while the alertcan occur, additional information can be included that can allow a userto determine if a response is appropriate, e.g., in this example, arepair crew might not be dispatched where the outlier condition isattributable to the presence of the political rally. In another example,where an upgrade has been applied to a RAN device, an outlier can bedetermined related to improved performance, e.g., data throughput forthe upgraded RAN device can increase, dropped call numbers can decrease,etc., which can enable tracking network management efforts, etc.

In an aspect, system 100 can enable outlier analysis to be performed inview of historical metrics associated with a RAN device, e.g., macro RANdevice, femtocells, etc. Historical metrics, as disclosed herein, can berelated to historically similar environments for a RAN device, e.g.,historical metrics from the RAN device in similar conditions can provideinformation of higher relevance. Moreover, historical RAN data can be ofnearly any depth, e.g., it may be none, some or all historical data forthe RAN device being analyzed. As an example, a current RAN data valuecan be analyzed in view of 12 data points for a historically similarenvironment for the RAN device, such as connected call data for todayfrom 8 am to 9 am and from the last 12 business days from 8 am to 9 am,or from this Monday from 8 am to 5 pm and the previous 12 Mondays from 8am to 5 pm, etc. Of note, more granular metrics, e.g., a 5-minute windowas compared to a 1-hour window, an 8-hour window, a 24-hour window,etc., can provide increased specificity, e.g., when an outlier occurs,cause of an outlier event, etc., but can be expected to increase thefrequency of outlier analysis process.

Wherein the disclosed subject matter is scalable, outlier analysis canbe performed as frequently as needed for a given computational budget,e.g., more frequent outlier analysis that can yield increasinglygranular outlier detection, can be accommodated by higher speedprocessors and/or distributed computing technologies, e.g., intervalanalysis of devices comprising a RAN for a national cellular providercan typically be performed more frequently in a distributed computingenvironment or on clusters of machines than can be performed on a singledevice, more especially in view of general economic aspects associatedwith deploying a single machine capable of these large scale computingtasks. However, in another example, a single machine can employ thesetechniques where less frequent, and less granular outlier analysis, isneeded for fewer RAN devices, e.g., a femtocell access point (FAP) canperform outlier analysis and then communicate relevant results to acentral system in near real time within the scope of the disclosedsubject matter. In an aspect, historical data can become stale and be orlesser value, however, the depth of historical data can be anengineering decision, e.g., which can be determined and included in aprofile, e.g., via performance profile component 360, 460, etc. As anexample, while a large number of historical values can exist for a RANdevice, such as 1.5M values, a relevant number can be a smaller numberof values, such as 1, 2, 5, 8, 12, 60, 100, 1000, etc., wherein thenumber of computations can be reduced with a lower number of valueswhile still retaining sufficient accuracy to determine an outliercondition related to the RAN metric under analysis, e.g., where 60values results in similar outlier detection to 1.5M values,determinations made with 60 values can be significantly lesscomputationally intensive than processing the 1.5M values. Similarly,where 12 values are sufficient in view of defined analytical goals,processing 12 values can be less computationally intense than processing60 values. Where RAN metric analysis can occur at a designated intervalto provide a desired granularity an outlier detection, and across anumber of devices, such as those comprising an access network for anational-scale wireless carrier, balancing the number of computationsagainst the cost and provisioning of those computations can be animportant consideration. The instant disclosure supports scalableanalysis, e.g., via distributed computation, etc., that can allowincreases/decreases in historical data depth, number of RAN devicesanalyzed, and granularity of outlier detection in a controllable mannerallowing for adapting the complexity of analysis in view of otherconsiderations such as cost, time, resource consumption, etc.

FIG. 2 is a depiction of a system 200 that can facilitate responding toa determined RAN performance condition based on performance outlieranalysis in accordance with aspects of the subject disclosure. System200 can comprise POAC 220. POAC 220 can receive RAN data, notillustrated. RAN data can comprise current information related to a RANdevice. RAN data that can comprise KPI data 204 related to RAN 222. KPIdata 204 can further comprise historic KPI information related to thehistorical performance of RAN device 222. As examples, KPI data 204 cancomprise information related to events such as establishing and/ormaintaining a call/data session, handoffs, dropped calls/data sessions,reestablished calls/data sessions, throughput, jitter, bandwidth, deviceidentification, RAN device environment, e.g., time, date, weather,events, network traffic, vehicular or pedestrian traffic, softwareversions, operational uptime, repair information, hardwarecharacteristics including temperature, versions, voltage, current,power, etc., position, rankings, self-organizing network information,network routing information, etc. Nearly any data associated with theoperation of RAN device 222 or the environment and characteristics ofRAN device 222 or an associated network can be part of KPI data 204 andcan be current or historical in nature.

POAC 220 can determine, based on KPI data 204, performance aspects ofRAN device 222 or an associated network, including information relatedto unusual behavior, e.g., a performance outlier. A performance outliercan be related to RAN device metrics that are outside of a normaloperation parameter. In an aspect, a performance outlier can be directlyidentified where a metric is determined to satisfy a rule related to anormal value ascribed to the associated performance parameter, forexample, a higher power consumption at RAN device 222 can be determinedto be a performance outlier, e.g., it can be determined that RAN device222 is drawing more power than manufactures specifications indicate thedevice should be drawing. In another aspect, a performance outlier canbe determined based on analysis of KPI data 204 to determine atypicalbehavior, e.g., a high level of call events can be analyzed to determinethat traffic via RAN device 222 has increased and can be flagged as aperformance outlier. This analytical aspect can be expanded to determineoutliers based on historical performance. As an example, where RANdevice 222 historically supports 1000 call events per hour based onhistorical data, a KPI indicating 1800 call events can be determined tobe an outlier. In an aspect, the instant disclosure can enabledetermination of normal behavior for a RAN device with regard tohistorically similar environments. As an example, where RAN device 222typically supports 1000 call events per hour on weekends, 1800 eventsper hour on a weekday can be uncorrelated due to a dissimilar historicalenvironment, e.g., weekend event counts can be distinct historicallyfrom weekday event counts. However, where a current weekend event countis, for example 2200 events per hour, this can be determined to be aperformance outlier in view of the historically similar environmentdata, e.g., the historical weekend event count being around 1000 eventsper hour.

POAC 220 can be employed to automate outlier analysis for RAN devices,e.g., RAN device 222, etc. This can support scalable application ofoutlier analysis to a plurality of RAN devices, e.g., the large numberof RAN devices, including RAN device 222, associated with a wirelesscarrier access network, etc. Moreover, POAC 220 can facilitate analysisof large amounts of data, such as can be received from a wireless accessnetwork at the carrier or enterprise level. In some embodiments, massiveamounts of data can be quickly processed in an efficient manner byemploying distributed computing paradigms, for example, on a Linuxcluster or other distributed computing system.

POAC 220 can facilitate access to performance outlier information 290.Performance outlier information 290 can comprise information related toanalysis of KPI data 204 via POAC 220. In some embodiments, performanceoutlier information 290 can comprise identification of RAN device 222and a determined outlying parameter, such as related to sessions betweenmobile device 220 and RAN device 222, etc. As an example, performanceoutlier information 290 can comprise a list of RAN devices determined tohave outlying operation values, including RAN device 222, to aid indirecting resources to respond to the outlying operational parameter,e.g., repairs, upgrades, resets, etc. Performance outlier information290 can comprise, in some embodiments, other information related to RANdevice 222 or analysis of KPI data 204. In a related embodiment,performance outlier information 290 can comprise information related toa historically similar environment for RAN device 222, identification ofan event associated with evolution of an outlier value, an alert, aflag, a request for response, etc. As an example, performance outlierinformation 290 can include an alarm trigger, a determined outliervalue, identification of RAN device 222, and information related to anenvironment of RAN device 222 to aid in identifying historically similarenvironments for RAN device 222. This example illustrates that while thealert can occur, additional information can be included that can allowother devices/users to determine if a response is needed.

System 200 can further comprise carrier planning component 299 that canreceive performance outlier information 290. Carrier planning component299 can support or enable planned allocation of carrier resources, suchas allocation of a repair resource in response to a determinedperformance outlier condition. In an embodiment, carrier planningcomponent 299 can enable access to service ticket data 292. Serviceticket data 292 can comprise information facilitating applying serviceto RAN device 222. The application of service to RAN device 222 can, insome embodiments, be related to responding to a determined serviceoutlier, e.g., determined via POAC 220 based on KPI data 204 anddetermined to be actionable via carrier planning component 299.

Dispatching component 294, of system 200, can receive service ticketdata 292 and, in response, can cause service alert 294 to be received bya technician, e.g., a repair or service vendor technician, that canperform service visit 296 to RAN device 222. As such, system 200illustrates effecting a response to determining an outlying value in KPIdata 204 related to RAN device 222. The response can comprisedetermining the presence of an outlier value, e.g., based on ahistorically similar environment for RAN device 222, assessing theimpact of the determined outlier via carrier planning component 299, andtriggering a response, e.g., generating a service ticket. The serviceticket can cause service to be given to RAN device 222, e.g., viadispatching component 294 causing a service alert and a correspondingservice visit being undertaken. In some embodiments, dispatchingcomponent 294 can be part of a system different from a network carriersystem, e.g., a vendor system, a contractor system, etc. Similarly,service alert 294 can be responded to by third party servicetechnicians. In some embodiments, KPI data 204 can be made available toPOAC 220 via third party systems, e.g., RAN device 222 can be aprivately operated RAN device and associated KPI data can be sent fromthe private operator system to a network carrier system comprising POAC220. In other embodiments, RAN device 222 can be operated by a networkcarrier but packaging of KPI data 204 can be via a vendor or other thirdparty system before being delivered to POAC 220. In some embodiments,KPI data 204 can be received at POAC 220 from a plurality of sources,including a network carrier source and/or a non-network carrier source,for one or more RAN device including RAN device 222, e.g., some KPIs forRAN device 222 can be received as KPI data 204 by POAC 220 from networkcarrier systems while other KPIs for RAN device 222 can be received asKPI data 204 by POAC 220 from non-network carrier systems.

In an aspect, system 200 can enable outlier analysis to be performed inview of historical metrics associated with RAN device 222. Historicalmetrics, as disclosed herein, can be related to historically similarenvironments for RAN device 222, e.g., historical metrics from RANdevice 222 in similar operating conditions can provide more relevantinformation than historical information for dissimilar operatingconditions. Moreover, historical RAN data can be of nearly any depth,e.g., it may be none, some or all historical data for RAN device 222.Whereas outlier analysis, as disclosed, is scalable, it can be performedas frequently as needed for a desired level of granularity, e.g., morefrequent outlier analysis that can yield increasingly granular outlierdetection. Moreover, highly granular outlier detection can beaccommodated by high speed processors and/or distributed computingtechnologies, e.g., shorter intervals of analysis, for example, every 5minutes rather than every 5 hours, can typically be more computationallyintensive and can be performed efficiently in a scalable distributedcomputing environment, e.g., on clusters of machines enabling scaling ofprocessors assigned to the analysis. Distributed computing can be morecost effective than deploying a single machine capable of thesevoluminous computing tasks. Of note, while distributed computing isexpressly recited, it does not foreclose use of a single machine, e.g.,where the single machine is capable of the typically large volumes ofdata to be analyzed, or where less granular outlier analysis isacceptable, e.g., for fewer RAN devices, less frequent analysis, lesshistorical data depth, etc.

FIG. 3 illustrates a system 300 that facilitates access to performanceoutlier information based on performance outlier analysis of current andhistorical RAN information in accordance with aspects of the subjectdisclosure. System 300 can comprise POAC 320. POAC 320 can receiveinstant RAN data 302 and historical RAN data 303. Instant RAN data 302can comprise current information related to a RAN device. In someembodiments, instant RAN data 302 can comprise KPIs related to operationof a RAN device. Historical RAN data 303 can comprise historicinformation related to a RAN device and, in some embodiments, cancomprise historical KPIs. In an aspect, historical RAN data 303 cancomprise some, none, or all, historical RAN data for one or more RANdevices related to outlier analysis in response to POAC 320 receivinginstant RAN data 302. In some embodiments historical RAN data 303 cancomprise historical information related to a RAN determined to berelevant to an outlier analysis by POAC 320. In certain embodiments POAC320 can designate what historical RAN information is to be received ashistorical RAN data 303, e.g., POAC 320 can request historical RANinformation of a determined depth, related to a RAN environmentdetermined to be historically similar, etc., which can then be received,such as from a data store, as historical RAN data 303. In otherembodiments, Historical RAN data 303 can be related to a RAN devicerelated to the outlier analysis and POAC 320 can filter or processhistorical RAN data 303 to employ relevant data contained therein.Nearly any data associated with the operation of a RAN device or theenvironment and characteristics of the RAN device or associated networkcan be part of instant RAN data 302 and/or historical RAN data 303. Ofnote, historical RAN data 303 can be received from nearly any source,e.g., a RAN device can store its own historical information and provideaccess thereto when requested, historical RAN information can becompiled on carrier-side servers or other data stores, historical RANinformation can be warehoused in third-party data stores, historical RANinformation can be stored across one or more data stores communicativelycoupled to POAC 320, etc.

POAC 320 can determine, based on instant RAN data 302 and historical RANdata 303, performance aspects of a RAN device or associated network,including information related to atypical or anomalistic behavior, e.g.,a performance outlier. A performance outlier can be related to RANdevice related values that are outside of a determined or specifiednormal operation parameter. In an aspect, a performance outlier can bedirectly identified from instant RAN data 302 where a value isdetermined to satisfy a rule related to the normal value ascribed to theassociated performance parameter, e.g., wherein the normal value isdetermined without the use of historical RAN data 303, for example, acurrent drop in a power supply of a RAN device can be determined to be aperformance outlier where the power supply is rated by the manufacturerto provide a specified minimum current, e.g., the current drop can beascribed to a failing power supply without reliance on historical RANdata 303. In another aspect, a performance outlier can be determinedbased on analysis of Instant RAN data 302 and historical RAN data 303 todetermine atypical behavior, e.g., an outlier analysis can determineoutliers based on instant performance metrics in view of historicalperformance metrics, including historical performance metrics that canbe considered highly relevant, e.g., historical performance metricsrelated to historically similar environments.

POAC 320 can comprise instant performance component 340 that candetermine values for metrics of interest from instant RAN data 302 forcurrent RAN operations. As an example, instant performance component 340can determine a number of failed sessions, a total of session events,and compute a failure rate based thereon, from instant RAN data 302being received from one or more RAN device. In an aspect, instant RANdata 302 can be a burst or continuous flow of data, e.g., a RAN devicecan send the last hour of performance data to POAC 320 in a short burstof data at one hour intervals, or can send data as it occurs to in apseudo-continuous trickle of data that can be compiled at POAC 320. Theexample one last hour of data can be considered instant data incomparison of historical RAN data 303 that can be distinct in that itcan reflect data for the same hour but for previous days and, moreespecially, for previously similar days, e.g., data collected that isfrom a historically similar environment for the RAN device.

Performance analysis component 350 of POAC 320 can receive instantperformance information from instant performance component 340 and canreceive historical RAN data 303. Performance analysis component 350 candetermine performance outlier information 390 based on the informationreceived from instant performance component 340 and historical RAN data303. In an aspect, an outlier condition can indicate when the metric isdetermined to satisfy a rule related to a trigger value. The triggervalue can be received from performance profile component 360 that can beupdated via profile data 362. As an example, a rho value of 0.9 can beset via profile data 362 at performance profile component 360, which canbe accessed by performance analysis component 350, such that wheninstant performance information and historical RAN data 303 result in arho value of greater than 0.9, the related metric can be treated asindicating an outlier condition. This example outlier condition can thenbe responded to, e.g., being reported out as part of performance outlierinformation 390, etc.

Performance outlier response component 370 can receive performanceoutlier information 390 and can determine performance outlier responseinformation 372. Performance outlier response information 372 cancomprise information related to a response to a determined outliercondition. As examples, performance outlier response information 372 cancomprise information initiating an alert, information adapted for use ina planning system such as carrier planning component 299, etc.,information initiating a service visit, etc. In some embodiments,performance outlier response component 370 can be comprised in POAC 320and can receive updates via performance profile component 360, notillustrated, based on profile data 362, e.g., profile data can indicateor designate both trigger values and/or processes for generatingperformance outlier response informing 372 based on performance outlierinformation 390. Of note, performance outlier information 390 can differfrom performance outlier response information 372 in that the prior canindicate the outlier, the RAN, the value, the environment, etc., relatedto the determination of the outlier condition itself, while the latercan indicate how to address the determined outlier condition, e.g.,based on the particular outlier and value, should an alert be caused,should a crew be dispatched, can the condition be logged and tracked,etc.

As disclosed elsewhere herein, POAC 320 can be employed to automateoutlier analysis for RAN devices. This can support scalable applicationof outlier analysis to a plurality of RAN devices. Moreover, POAC 320can facilitate analysis of large amounts of data, such as can bereceived from a wireless access network at the carrier or enterpriselevel. In some embodiments, massive amounts of data can be quicklyprocessed in an efficient manner by employing distributed computingparadigms, for example, on a Linux cluster with Spark data nodes. Wherea massive data store can archive performance records for RAN devicescomprising a wireless access network, POAC 320 can select relevant datatherefrom, in view of instant RAN data 302 metrics determined by instantperformance component 340. This historically similar environmentinformation can be comprised in historical RAN dada 303 and can beanalyzed in view of current RAN performance and an updateable profilefor defining an outlier condition. Performance profile component 360 canallow for outlier definition profiles that set conditions, that can bethe same or different, both for the same or different metrics on one ormore RAN devices, e.g., an outlier can be defined as a change of morethan 1% for dropped calls at a first RAN device and more than 1.25% fordropped calls at a second RAN device, while another outlier can bedefined as a change of more than 100 session events/hour for both thefirst and second RAN device. Moreover, performance outlier responsecomponent 370 can respond to a determined outlier and provide access toinformation related to a determined response. The response can, in someembodiments, also be determined based on values in a profile, e.g., viaperformance profile component 360. Of note, more than one profile can bestored, allowing for rapid changes between profiles, e.g., a testprofile, a normal operation profile, a malware response profile, etc.,which can be manually or automatically selected, e.g., a test engineercan employ a test profile on demand, or a malware profile canautomatically be selected when malware or another cyber attack isdetected on the RAN, etc.

POAC 320 can facilitate access to performance outlier information 390.Performance outlier information 390 can comprise information related toanalysis of Instant RAN data 302 and/or historical RAN data 303 via POAC320. In some embodiments, performance outlier information 390 cancomprise identification of a RAN device determined to have an outlyingparameter or condition. As an example, performance outlier information390 can comprise a list of RAN devices determined to have outlyingoperation values to aid in directing resources to correcting theoutlying operation, e.g., repairs, upgrades, resets, etc. Performanceoutlier information 390 can comprise, in some embodiments, otherinformation related to a RAN device, analysis of Instant RAN data 302,historical RAN data 303, etc. In a related embodiment, performanceoutlier information 390 can comprise information related to ahistorically similar environment for a RAN associated with an outliervalue, identification of an event associated with evolution of anoutlier value, an alert, a flag, a request for response, etc. Some,none, or all of this information can also be included in performanceoutlier response information 372. As an example, performance outlierresponse information 372 can include an alarm trigger, a determinedoutlier value, identification of the RAN device associated with theoutlier value, and information indicating that a professional basketballgame was in progress within the coverage area of the identified RANdevice proximate in time to the determined outlier event. As such,performance outlier response information 372 can comprise instructionsto cause logging of the outlier and monitoring of the condition withmore restrictive outlier trigger values rather than containinginstructions to dispatch a repair crew.

In an aspect, system 300 can enable outlier analysis to be performed inview of historical metrics associated with a RAN device and in view ofan outlier detection profile and/or an outlier response profile. Ofnote, historical RAN data can be of nearly any depth, e.g., it may benone, some or all historical data for the RAN device being analyzed.Further of note, system 300 is scalable and outlier analysis can beperformed as frequently as needed for a given computational budget,e.g., more frequent outlier analysis that can yield increasinglygranular outlier detection. This can be accommodated by higher speedprocessors and/or distributed computing technologies. In an aspect,historical data can become stale and be of lesser value, however, thedepth of historical data can be an engineering decision, e.g., which canbe determined and included in a profile, e.g., via performance profilecomponent 360, 460, etc. This can allow stale data to be excluded from arequest related to accessing historical RAN data 303, filtered, orotherwise discounted. In some embodiments, older data can be weighted tohave less effect, or conversely, newer data can be weighted to have moreeffect, in determining an outlier condition. Moreover, historical dataof a certain age or determined lack of relevance can simply be discarded(or archived) from a data store, e.g., there is probably little sense inmaintaining active historical RAN data storage for a RAN device that wasreplaced with a newer RAN device 15 years ago. As such, POAC 320 canaccess historical RAN data 303 from a catchall data store or can accessdata from a maintained data store that can be scrubbed and pruned tofacilitate faster access to relevant data.

FIG. 4 illustrates a system 400 that facilitates access to performanceoutlier information based on performance outlier analysis of current RANinformation and historical RAN information received from a data store inaccordance with aspects of the subject disclosure. System 400 cancomprise a POAC 420. POAC 420 can receive instant RAN data 402. RAN data402 can comprise current information related to a RAN device. In someembodiments, RAN data 402 can comprise KPIs related to operation of aRAN device. In an aspect, RAN data 402 can comprise historical RAN datarelated to a RAN device and, in some embodiments, can comprisehistorical KPIs. In an aspect, historical RAN data can comprise some,none, or all, historical RAN data for one or more RAN devices related tooutlier analysis in response to POAC 420 receiving RAN data 402. In someembodiments, historical RAN data can comprise historical informationrelated to a RAN determined to be relevant to an outlier analysis byPOAC 420. Nearly any data associated with the operation of a RAN deviceor the environment and characteristics of the RAN device or associatednetwork can be part of RAN data 402 and/or historical RAN data. Of note,historical RAN data can be received from nearly any source, e.g., a RANdevice can store its own historical information and provide accessthereto when requested, historical RAN information can be compiled oncarrier-side servers or other data stores, historical RAN informationcan be warehoused in third-party data stores, historical RAN informationcan be stored across one or more data stores communicatively coupled toPOAC 420, etc.

POAC 420 can comprise historical performance component 480 that canreceive RAN data 402, including historical RAN data comprised therein.Historical performance component 480 that can facilitate filtering ofhistorical RAN data from RAN data 402. In some embodiments, historicalperformance component 480 that can determine what historical RAN data isrelated to current performance, e.g., as determined by performancecomponent 440, and facilitate accessing said relevant historical RANdata via RAN data 402, e.g., relevant historical RAN data can be relatedto a historical similar environment and historical performance component480 that can facilitate designating that such data is included in RANdata 402 received by POAC 420. In certain embodiments POAC 420 candesignate what historical RAN information is to be received ashistorical RAN data comprised in RAN data 402, e.g., POAC 420 canrequest historical RAN information of a determined depth, related to aRAN environment determined to be historically similar, etc., which canthen be received, such as from a data store, as part of RAN data 402. Inother embodiments, historical RAN data can be included in RAN data 402and historical performance component 480 that can facilitate filteringRAN data 402 to employ historical RAN data that is deemed relevant,e.g., based on selecting historically similar environment RAN data fromthe included historical RAN data of RAN data 402. Historical performancecomponent 480 that can further facilitate the storage of data via datastore 482. In a further aspect, historical RAN data previously stored ondata store 482 can be retrieved by historical performance component 480as part of an outlier analysis of RAN data 402 by POAC 420, e.g., viaperformance analysis component 450.

POAC 420 can determine, based on RAN data 402 and historical RAN data,performance aspects of a RAN device or associated network, includinginformation related to atypical or anomalistic behavior, e.g., aperformance outlier. A performance outlier can be related to RAN devicerelated values that are outside of a determined or specified normaloperation parameter. In an aspect, a performance outlier can be directlyidentified from RAN data 402 where a value is determined to satisfy arule related to the normal value ascribed to the associated performanceparameter, e.g., wherein the normal value is determined without the useof historical RAN data. In another aspect, a performance outlier can bedetermined based on analysis of RAN data 402 and historical RAN data todetermine atypical behavior, e.g., an outlier analysis can determineoutliers based on instant performance metrics in view of historicalperformance metrics, including historical performance metrics that canbe considered highly relevant, e.g., historical performance metricsrelated to historically similar environments.

POAC 420 can comprise performance component 440 that can determinevalues for metrics of interest from RAN data 402 for current RANoperations. As an example, performance component 440 can determine anumber of failed sessions, a total of session events, and compute afailure rate based thereon, from RAN data 402 being received from one ormore RAN device. In an aspect, RAN data 402 can be a burst or continuousflow of data. The example RAN data 402 related to a current/instantstate of a RAN device can be considered instant data in comparison tohistorical RAN data that can be distinct because it reflects data forthe same or similar RAN environments but for previousstates/events/periods, e.g., a same 2-hour window for previous days and,more especially, for previously similar days, such as for the lasttwelve Tuesdays from 9 am to 11 am.

Performance analysis component 450 of POAC 420 can receive performanceinformation from performance component 440 and can receive historicalRAN data from historical performance component 480, e.g., extracted fromRAN data 402 and/or retrieved from data store 482. Performance analysiscomponent 450 can determine performance outlier information 490 based onthe information received from performance component 440 and historicalRAN data. In an aspect, an outlier condition can be indicated when ametric is determined to satisfy a rule related to a trigger value. Thetrigger value can be comprised in a profile accessible via performanceprofile component 460, and which profile can be updated with profiledata 462. As an example, a rho value can be set in a first profile inaccordance with profile data 462 via performance profile component 460,which can be accessed by performance analysis component 450, such thatwhen performance information and historical RAN data result in a rhovalue of greater than the set value, the related metric can bedesignated as an outlier. This example outlier condition can then beresponded to, e.g., being reported out as part of performance outlierinformation 490, etc.

Performance outlier response component 470 can receive performanceoutlier information 490 and can determine performance outlier responseinformation 472. Performance outlier response information 472 cancomprise information related to a response to a determined outliercondition. As examples, performance outlier response information 472 cancomprise information initiating an alert, information adapted for use ina planning system such as carrier planning component 299, etc.,information initiating a service visit, etc. In some embodiments,performance outlier response component 470 can be comprised in POAC 420and can receive updates via performance profile component 460, updatenot illustrated for clarity, based on profile data 462, e.g., profiledata can indicate or designate both trigger values and/or processes forgenerating performance outlier response information 472 based onperformance outlier information 490. Of note, performance outlierinformation 490 can differ from performance outlier response information472 in that the prior can indicate the outlier, the RAN, the value, theenvironment, etc., related to the determination of the outlier conditionitself, while the later can indicate how to address the determinedoutlier condition, e.g., based on the particular outlier and value,should an alert be caused, should a crew be dispatched, can thecondition be logged and tracked, etc.

As disclosed elsewhere herein, POAC 420 can be employed to automateoutlier analysis for RAN devices. This can support scalable applicationof outlier analysis. Moreover, POAC 420 can facilitate analysis of largeamounts of data, such as can be received from a wireless access networkat the carrier or enterprise level. In some embodiments, massive amountsof data can be quickly processed in an efficient manner by employingdistributed computing paradigms, for example, on a Linux cluster withSpark data nodes. Where a massive data store can archive performancerecords for RAN devices comprising a wireless access network, POAC 420can select relevant data therefrom, in view of RAN data 402 metricsdetermined by performance component 440. This historically similarenvironment information can be comprised in RAN data, e.g., thehistorical data and the instant data can be comprised in RAN data 402,and can be analyzed in view of current RAN performance and an updateableprofile for defining an outlier condition. Performance profile component460 can allow for outlier definition profiles that set conditions, thatcan be the same or different, both for the same or different metrics onone or more RAN devices. Moreover, performance outlier responsecomponent 470 can respond to a determined outlier and provide access toinformation related to a determined response. The response can, in someembodiments, also be determined based on values in a profile, e.g., viaperformance profile component 460. Of note, more than one profile can bestored, allowing for rapid changes between profiles.

POAC 420 can facilitate access to performance outlier information 490.Performance outlier information 490 can comprise information related toanalysis of RAN data 402 and/or historical RAN data via POAC 420. Insome embodiments, performance outlier information 490 can compriseidentification of a RAN device determined to have an outlying parameteror condition. As an example, performance outlier information 490 cancomprise a list of RAN devices determined to have outlying operationvalues to aid in directing resources to correcting the outlyingoperation, e.g., repairs, upgrades, resets, etc. Performance outlierinformation 490 can comprise, in some embodiments, other informationrelated to a RAN device, analysis of RAN data 402, historical RAN data,etc. In a related embodiment, performance outlier information 490 cancomprise information related to a historically similar environment for aRAN associated with an outlier value, identification of an eventassociated with evolution of an outlier value, an alert, a flag, arequest for response, etc. Some, none, or all of this information canalso be included in performance outlier response information 472.

In an aspect, system 400 can enable outlier analysis to be performed inview of historical metrics associated with a RAN device and in view ofan outlier detection profile and/or an outlier response profile. Ofnote, historical RAN data can be of nearly any depth, e.g., it may benone, some or all historical data for the RAN device being analyzed.Further, of note, system 400 can be scalable and outlier analysis can beperformed as frequently as needed for a given computational budget. Thiscan be accommodated by higher speed processors and/or distributedcomputing technologies. In an aspect, historical data can become staleand be of lesser value, however, the depth of historical data can be anengineering decision, e.g., which can be determined and included in anupdateable profile, e.g., accessed via performance profile component360, 460, etc. This can allow stale data to be excluded from a requestrelated to accessing historical RAN data, filtered, or otherwisediscounted. In some embodiments, older data can be weighted to have lesseffect, or conversely, newer data can be weighted to have more effect,in determining an outlier condition. Moreover, historical data of acertain age or determined lack of relevance can simply be discarded (orarchived) from a data store. As such, POAC 420 can access historical RANdata from a catchall data store or can access data from a maintaineddata store that can be scrubbed and pruned to facilitate faster accessto relevant data.

FIG. 5 illustrates a system 500 that facilitates enabling access toperformance outlier information determined via a distributed computingenvironment in accordance with aspects of the subject disclosure. System500 can comprise POAC 520 that can receive RAN data 502 and enableaccess to performance outlier information 590. RAN data 502 can comprisecurrent information related to a RAN device. In some embodiments, RANdata 502 can comprise KPIs related to operation of a RAN device. In anaspect, RAN data 502 can comprise historical RAN data related to a RANdevice and, in some embodiments, can comprise historical KPIs. In anaspect, historical RAN data can comprise some, none, or all, historicalRAN data for one or more RAN devices related to outlier analysis inresponse to POAC 520 receiving RAN data 502. In some embodiments,historical RAN data can comprise historical information related to a RANdetermined to be relevant to an outlier analysis by POAC 520. Nearly anydata associated with the operation of a RAN device or the environmentand characteristics of the RAN device or associated network can be partof RAN data 502 and/or historical RAN data. Of note, historical RAN datacan be received from nearly any source, e.g., a RAN device can store itsown historical information and provide access thereto when requested,e.g., via data store 582, etc., historical RAN information can becompiled on carrier-side servers or other data stores, e.g., via datastore 584, etc., historical RAN information can be warehoused inthird-party data stores, e.g., via data store 583, etc., historical RANinformation can be stored across one or more data stores communicativelycoupled to POAC 520, etc.

POAC 520 can determine and facilitate access to performance aspects of aRAN device or associated network, including information related toatypical or anomalistic behavior, e.g., a performance outlier, based onRAN data 502, which can comprise historical RAN data, and historical RANdata access via other sources, e.g., data store 582-584, etc.Performance outlier information 590 can comprise information related toRAN device related values that are outside of a determined or specifiednormal operation parameter, e.g., a performance outlier. In an aspect, aperformance outlier can be directly identified from RAN data 502 where avalue is determined to satisfy a rule related to the normal valueascribed to the associated performance parameter, e.g., wherein thenormal value is determined without the use of historical RAN data. Inanother aspect, a performance outlier can be determined based onanalysis of RAN data 502 and historical RAN data to determine atypicalbehavior, e.g., an outlier analysis can determine outliers based oninstant performance metrics in view of historical performance metrics,including historical performance metrics that can be considered highlyrelevant, e.g., historical performance metrics related to historicallysimilar environments.

POAC 520 can be communicatively coupled to POAC 522 and/or POAC 524,etc., for example, as illustrated in system 500. Of note, othercommunicative coupling schema are within the scope of the instantdisclosure, though are not recited for the sake of clarity and brevity.This can support scalable application of outlier analysis, e.g., viadistributed computation, etc. POAC 520 can facilitate analysis of largeamounts of data, such as can be received from a wireless access networkat the carrier or enterprise level. In some embodiments, outlieranalysis can be processed by employing distributed computing paradigms,for example, on a Linux cluster with Spark data nodes. Where one or moredata stores, e.g., 582-584, etc., can archive performance records forone or more RAN devices, POAC 520 can select, or instruct other devicesto select, relevant data therefrom, in view of RAN data 502.Historically similar environment information can be comprised in RANdata and can be analyzed in view of current RAN performance and aprofile for defining an outlier condition. POAC 520 can delegate outlieranalysis tasks, e.g., computation, data retrieval/filtering, etc., toother device, e.g., POAC 522, 524, etc., to allow for outlierdetermination to be processed across one or more devices, e.g.,distribution of outlier analysis across POAC 520-524, etc., based on RANdata 502 and enabling access to performance outlier information 590. Asan example, POAC 520 can be a single device capable of processing datafor only a few RAN devices, POAC 522 can be a Linux cluster that canprocess data for several hundred RAN devices, and POAC 524 can be avirtualized POAC that can run on many clustered computing devices toprocess data for an unlimited number of RAN devices. In this example, itcan be cost efficient for a single user to operate POAC 520, butoperation of 522 and 524 can be overly resource intensive to bepracticable. Further, in this example, POAC 522 can be employed by aregional carrier entity for normal operations, while 524 remainsprohibitively resource intensive. Still further in this example, POAC524 can be operated by a third party vendor servicing several regionalcarrier entities, can be operated by a national carrier entity, etc.Where outlier detection at a granularity that exceeds POAC 520capabilities, operations can be shared/distributed to POAC 522 and/or524. Similarly, where outlier detection exceeds POAC 522 capabilities,operations can be shared/distributed to POAC 524. As an example, where aregional carrier acquires another regional carrier, outlier analysis forthe increased number of RAN devices in the expanded coverage can beaccommodated by distributing operations to additional POAC, e.g., 524,etc., by reducing granularity of the analyses, selecting an alternateanalysis scheme, analyzing outlier information for less than all of theRAN devices, etc. The scalable nature of the present disclosure allowsfor ready adaption of POAC devices, e.g., 520-524, to accommodate afluctuating number of RAN devices, changes in desired granularity ofdata, changes in accuracy of outlier data, etc.

In an aspect, a POAC, e.g., 582, can comprise a data store, e.g., 582,that can enable storage of historic RAN data at the POAC. In someembodiments, data store 582 can store some, all, or no data related toRAN devices being analyzed by POAC 522, e.g., storing data locally canallow ready access to relevant data, e.g., historically similarenvironment data is already stored locally in this scenario. In otherembodiments, data store 582 can store filtered data, e.g., bulk data isstored elsewhere, e.g., data store 584, etc., and relevant historic RANdata can be stored locally, e.g., at data store 582, etc.

In another aspect, a POAC, e.g., 584, can be communicatively coupled toa data store, e.g., 583, that can enable storage of historic RAN dataproximate to the POAC, though not necessarily as part of the POAC. Insome embodiments, data store 583 can store some, all, or no data relatedto RAN devices being analyzed by POAC 524, e.g., storing data locallycan allow ready access to relevant data, e.g., historically similarenvironment data is already stored near to the POAC in this scenario. Inother embodiments, data store 583 can store filtered data, e.g., bulkdata is stored elsewhere, e.g., data store 584, etc., and relevanthistoric RAN data is stored proximate to the POAC, e.g., data store 583,etc.

In a further aspect, data stored on data store(s) 582-584, etc., can beaccessible to one or more of the POAC(s) 520-524, etc., to facilitateoutlier analysis. As an example, data store 584, accessed by POAC 520via communication framework 504, can store archival historic RAN data,data store 582, accessed by POAC 520 via POAC 522, can store all datafor RANs connected to POAC 520 for the last 6 months. This can allowPOAC 520 to perform outlier analysis quickly for local RAN devices, alittle more slowly for RAN devices needing data from data store 583 anda little more slowly for archival data on data store 584. In anotherexample, data store 584 can receive and store all RAN data from each ofPOAC 520-524 as RAN data is determined and this data can then beaccessed by each of POAC 520-524 in determining outlier information,noting that POAC 520 (without local storage) moves all data for theanalysis across the network in real time, POAC 522 with internal storage(582) can cache data for outlier analysis and can access other data viadata store 584 as needed, and POAC 524 can cache presumably more data onlocal external storage (583) data for most outlier analysis andpresumably request supplemental data from data store 584 the least. Thisexample illustrates low hardware cost (520) with high data burdens,moderate hardware cost (522/582) with moderate bandwidth burdens, andhigh hardware cost (524/583) with low bandwidth needs. It will be notedthat these examples are not exhaustive and nay and all otherconfigurations of distributed computing and data storage/transfer arewithin the scope of the instant disclosure though not explicitly recitedfor the sake of clarity and brevity.

FIG. 6 is a depiction 600 of example RAN performance data, an examplebinary array, example determined performance outlier information, and anexample data plot corresponding related thereto, in accordance withaspects of the subject disclosure in accordance with aspects of thesubject disclosure. Table 600 can comprise KPI values corresponding to13 KPI environments, e.g., columns KPI-1 through KPI-13, for a RANdevice metric. Of note, the example KPI values illustrated in FIG. 6,can be percentages of failures measured, e.g., KPI (%), so as tocorrespond to the values determined in example FIG. 7, however, KPIs canbe other than a percentage in both FIGS. 6 and 7 without departing fromthe disclosed subject matter, although not discussed for the sake ofclarity and brevity. A KPI environment can denote, for example, a date,wherein KPI-13 can be a call drop rate for today from noon to 1 pm,KPI-12 can be a call drop rate for one week earlier from noon-1 pm,KPI-11 can be for the same time window the week before that, etc., suchthat the same time window on the same day of the week is used in theanalysis, e.g., a historically similar environment for the RAN device.In another example, KPI-13 can be calls dropped during today's homefootball game, KPI-12 can be for calls dropped during the last homefootball game, KPI-11 can be for the home game before that, etc.,indicating that the environment can be historically similar even thoughthe time is not periodic in nature. Table 600 can further comprise 24rows of binary array data as illustrated.

Of note, other numbers of KPI environments illustrate is 13, e.g., 12historic plus one instant/current KPI value are represented in row KPI,columns KPI-1 through KPI-13. However, the present disclosure is not solimited and any number of KPI environments can be employed, e.g., 1column, 4 columns, 7 columns, 67 columns, 109 columns, 1.5M columns,etc. Moreover, the binary array portion of Table 600 illustrates a stairstep pattern, however the disclosure is again not limited to thispattern, and all binary array patterns are within the scope of thisinstant disclosure. The number of rows can be dependent on the number ofKPI environments, e.g., columns, employed. Table 600 displays 24 rows ofbinary array for completeness, though fewer rows can be employed wherethe correlation coefficients becomes duplicative, or can be greaterwhere the set of correlation coefficients can be incomplete.

Table 600, in the far right column, illustrates a computed correlationcoefficient based on the tabulated values illustrated. Of note, thecorrelation coefficient can be determined from the Pearson ProductMoment Correlation, though other correlation coefficients can be readilyused without departing from the scope of the instant disclosure, thoughnot explicitly discussed herein for the sake of clarity and brevity. Inaccord with Pearson Product Moment Correlation, there is a strongercorrelation where rho approaches +/−1, and less correlation as rhoapproaches 0. This correlation can be readily appreciated by plottingthe values for a row of tabulated data and determining a linear trendline for the plotted data, as depicted at 602 for Row 11 of thetabulated data in table 600. Plot 602 illustrates a comparatively strongcorrelation, rho=0.9495, in comparison to most of the other rho valuesin table 600, indicating a strong correlation between KPI-12 and KPI-13,and less correlation to KPI-1 through KPI-11. For the data in table 600,assuming a trigger value of rho>0.9, then row 11 indicates thatenvironments KPI-12 and KPI-13 correspond to outlier events. This canindicate that the metrics from those two RAN device environments can berelated to an issue with the RAN device that may need to be addressed.Further, this correlation can allow for computation of additionalinformation, e.g., that can also be included in performance outlierinformation 190-590, etc., as will be discussed in relation to thefollowing Figure.

FIG. 7 is a depiction 700 of example RAN performance data correspondingto an example performance outlier condition, and additional exampleperformance outlier information than can be determined in accordancewith aspects of the subject disclosure. Table 700 illustrates an examplecalculation of addition data that can be comprised in performanceoutlier information 190-590, etc. From table 600, the row with thehighest correlation coefficient can be selected, e.g., row 11, as the 01Pattern row in table 700. The KPI values from table 600 can be insertedas row KPI in table 700. Where, in this example, the KPI values arederived from a failure rate and an event rate, e.g., dropped call toplaced calls, failed handovers to attempted handovers, etc., thisadditional information can be included in rows Failures and Events oftable 700.

Taking note of the 01 Pattern row, KPI values for the KPI environments,e.g., KPI-1 through KPI-13 in this example, corresponding to the ‘0values’ can be considered uncorrelated and those corresponding to the ‘1values’ can be considered as correlated. The uncorrelated values can beconsidered ‘normal’ behavior for the RAN based on the historicallysimilar environments, while the correlated values can be considered‘outliers’. As such, a baseline KPI value can be determined for the RANdevice from the ‘normal behavior, e.g., the sum of the failures can bedivided by the sum of the events for the normal environment columns,e.g., the ‘0 value’ columns. This gives a baseline failure rate of 0.74%in view of normal behavior of the RAN device, as determined from thehistorically similar environments. The baseline failure rate can then bemultiplied with the events value of KPI-13 (and similarly with KPI-12,though not illustrated for clarity) to yield baseline failures for theRAN device at KPI-13, e.g., this is the number of failures attributableto the normal operation of the RAN device, in this example 7.73 of the35 failures would be considered normal/baseline. The baseline failurescan then be subtracted from the failures of KPI-13 to yield theadditional failures observed above the baseline rate, e.g., the extrafailure value, in this example, 27.27 of the 35 failures for the RANdevice at KPI-13 are outliers above the normal 7.73 failuresattributable to normal operation of the RAN device. Similardeterminations can be made for the RAN device at other KPIs, though notillustrated, yielding a corresponding baseline failures value, and acorresponding extra failure value. Of note, when corresponding valuesfor other KPIs are calculated, the count of previous KPIs selected viathe binary matrix illustrated can preferably be based on the same orsimilar number of events, e.g., where, in the above example, a row isselected based on the highest correlation coefficient using 13 KPIvalues (KPI-1 to KPI-13 to select row 11 based on the correlationcoefficient) and the baseline KPI value is determined from 11 of the 13KPI values (KPI-1 to KPI-11), then calculations of the extra failurevalue for other KPIs can be based on a similar 13 KPIs, such as wherethe extra failure value for KPI-14 (not illustrated) is determined, itcan be based on values selected from KPI-2 to KPI-13 based on the binarymatrix and the resulting correlation coefficients. In an aspect, thiscan allow the disclosed subject matter to capture historical effectsthat would not be propagated in more conventional statisticaltechniques, e.g., 3-sigma, standard deviation, etc. Where another ‘01pattern’ was associated with the highest correlation coefficient, the‘01 pattern’ used to select normal KPI values used in computing thebaseline KPI value can be different, which can result in a differentbaseline KPI value, baseline failure value, and a extra failure value.It will be noted that other values can be determined without departingfrom the scope of the present disclosure, though they are not discussedfor the sake of clarity and brevity. As an example, RAN sites that havean extra failure value transitioning a minimum extra failure value canbe flagged as acute offending RAN devices, etc.

In view of the example system(s) described above, example method(s) thatcan be implemented in accordance with the disclosed subject matter canbe better appreciated with reference to flowcharts in FIG. 8-FIG. 10.For purposes of simplicity of explanation, example methods disclosedherein are presented and described as a series of acts; however, it isto be understood and appreciated that the claimed subject matter is notlimited by the order of acts, as some acts may occur in different ordersand/or concurrently with other acts from that shown and describedherein. For example, one or more example methods disclosed herein couldalternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, interaction diagram(s) mayrepresent methods in accordance with the disclosed subject matter whendisparate entities enact disparate portions of the methods. Furthermore,not all illustrated acts may be required to implement a describedexample method in accordance with the subject specification. Furtheryet, two or more of the disclosed example methods can be implemented incombination with each other, to accomplish one or more aspects hereindescribed. It should be further appreciated that the example methodsdisclosed throughout the subject specification are capable of beingstored on an article of manufacture (e.g., a computer-readable medium)to allow transporting and transferring such methods to computers forexecution, and thus implementation, by a processor or for storage in amemory.

FIG. 8 illustrates a method 800 that facilitates initiating an alertbased on performance outlier analysis in accordance with aspects of thesubject disclosure. At 810, method 800 can comprise receiving radioaccess network (RAN) data. RAN data can comprise current informationrelated to a RAN device. RAN data can further comprise historicinformation related to a RAN device. In some embodiments, RAN data cancomprise key performance indicators (KPIs) related to operation of a RANdevice. As examples, RAN data can comprise information related to eventssuch as establishing and/or maintaining a call/data session, handoffs,dropped calls/data sessions, reestablished calls/data sessions,throughput, jitter, bandwidth, device identification, RAN deviceenvironment, e.g., time, date, weather, events, network traffic,vehicular or pedestrian traffic, software versions, operational uptime,repair information, hardware characteristics including temperature,versions, voltage, current, power, etc., position, rankings,self-organizing network information, network routing information, etc.Nearly any data associated with the operation of a RAN device or theenvironment and characteristics of the RAN device or associated networkcan be part of RAN data and can be current or historical in nature.

At 820, method 800 can comprise determining performance outlierinformation based on RAN data. This determination can be facilitated byanalysis with a binary array, for example, as depicted in table 600.Performance outlier information can comprise information related toperformance aspects of a RAN device or associated network, includinginformation related to atypical or anomalistic behavior, e.g., aperformance outlier. A performance outlier can be related to RAN devicerelated values that are outside of a determined or specified normaloperation parameter. In an aspect, a performance outlier can be directlyidentified where a value is determined to satisfy a rule related to thenormal value ascribed to the associated performance parameter. Inanother aspect, a performance outlier can be determined based onanalysis of RAN data to determine atypical behavior. This analyticalaspect can be expanded to determine outliers based on historicalperformance. In an aspect, the instant disclosure can enabledetermination of normal behavior for a RAN device with regard tohistorically similar environments.

Method 800 can include, at 830, enabling access to the performanceoutlier information. Performance outlier information can be employed byother devices or methods to respond to, track, log, etc., a performanceoutlier for a RAN device. In an aspect, this can facilitate correctionor mitigation of an underperforming RAN device. In another aspect, thiscan enable reporting of improvements related to alterations to a RAN orRAN device, e.g., where a software patch is rolled out to a RAN, animprovement in performance can be tracked where the RAN devices improveperformance over normal performance after they are patched. In a furtheraspect, access to performance outlier information can facilitateplanning for entities associated with a RAN, for example, tracking asemiannual KPI for RAN devices comprising a RAN can allow a networkoperator to determine that how quickly RAN devices are aging out basedon how frequently performance outliers arise, thereby allowing thenetwork operator to plan and budget for replacement of RAN devices at afuture time.

At 840, method 800 can comprise initiating an alarm condition. At thispoint method 800 can end. The initiating the alarm condition can be inresponse to performance outlier information being determined to satisfya rule related to a trigger condition. As an example, where performanceoutlier analysis indicates a measured level of dropped sessions exceedsa threshold value that is a multiple of a normal level of droppedsessions, an alarm can be triggered. As another example, where a RANdevice is determined to have multiple consecutive outlier values for thesame metric and the count satisfies a rule related to a trigger value,an alert can be sent to a repair department to facilitate sending atechnician to address the issue at the RAN device.

In an aspect, method 800 can be performed on a computing device oracross multiple computing devices, e.g., a distributed computing system.This can allow method 800 to effectively process related data in atimely and cost effective manner. Of note, this also allows method 800to scale, e.g., more computing tasks can be added to address more RANdevices, deeper historical data, more metrics, finer granularity ofoutlier detection, etc., fewer computing tasks can be performed toreduce costs, free up computer equipment, etc., where less granulardetection is acceptable, fewer RAN devices are monitored, more shallowhistorical data is employed, etc. As an example, method 800 can beperformed on an Apache™ Spark™ platform with cluster computing to allowfor spreading jobs across multiple data nodes in the cluster with dataredundancy, scalability, and in-memory processing with typicallylow-cost hardware and open source software. With sufficient scaling, thepresent disclosure can allow for near real-time detection of performanceoutliers, acute offender RAN devices, etc.

FIG. 9 illustrates a method 900 that facilitates determining performanceoutlier information based on performance outlier analysis of current andhistorical RAN information in accordance with aspects of the subjectdisclosure. At 910, method 900 can comprise receiving RAN data and aconstraint related to the RAN data from a RAN device. RAN data cancomprise current information related to a RAN device. In someembodiments, RAN data can comprise KPIs related to operation of a RANdevice. Nearly any data associated with the operation of a RAN device orthe environment and characteristics of the RAN device or associatednetwork can be part of RAN data and can be current or historical innature.

The constraint can be related to operating environment of the RANdevice. The constraint can enable selection of historically similarenvironment metrics. As an example, the constraint can be a RANidentifier, a time, a date, identification of an event, a location orproximity, traffic conditions proximate, geological information orweather conditions, solar activity information, etc. Nearly any datahelping to identify the conditions, e.g., environment of the RAN device,in which the values for analysis are captured can facilitate effectiveselection of other data captured in a historically similar environmentthat can increase the relevance of the other data to the analysis of theinstant data for outlier determination.

At 920, method 900 can comprise receiving historical RAN data. Thishistorical RAN data can be affiliated with the constraint and can bereceived from a data store. The data store can be located proximate toor remote from a processor executing elements of method 900, asdisclosed elsewhere herein. Historical RAN data can comprise historicinformation related to a RAN device and, in some embodiments, cancomprise historical KPIs. In an aspect, historical RAN data can comprisesome, none, or all, historical RAN data for one or more RAN devicesrelated to outlier analysis in response receiving RAN data at 910. Insome embodiments, historical RAN data can comprise historicalinformation related to a RAN determined to be relevant to an outlieranalysis. Of note, historical RAN data can be received from nearly anysource, e.g., a RAN device can store its own historical information andprovide access thereto when requested, historical RAN information can becompiled on carrier-side servers or other data stores, historical RANinformation can be warehoused in third-party data stores, historical RANinformation can be stored across one or more data stores communicativelycoupled to a device executing method 900. Historically relevant data canbe determined based, at least in part, on the constraint received at910.

At 930, method 900 can comprise, determining performance outlierinformation based on the RAN data and the historical RAN data. Thisdetermination can, in some embodiments, be facilitated by analysis witha binary array, for example, as depicted in table 600. Performanceoutlier information can comprise information related to performanceaspects of a RAN device or associated network, including informationrelated to atypical or anomalistic behavior, e.g., a performanceoutlier. A performance outlier can be related to RAN device relatedvalues that are outside of a determined or specified normal operationparameter. In an aspect, a performance outlier can be directlyidentified where a value is determined to satisfy a rule related to thenormal value ascribed to the associated performance parameter. Inanother aspect, a performance outlier can be determined based onanalysis of RAN data to determine atypical behavior. This analyticalaspect can be expanded to determine outliers based on historicalperformance. In an aspect, the instant disclosure can enabledetermination of normal behavior for a RAN device with regard tohistorically similar environments.

Method 900 can include, at 940, enabling access to the performanceoutlier information. Performance outlier information can be employed byother devices or methods to respond to, track, log, etc., a performanceoutlier for a RAN device. In an aspect, this can facilitate correctionor mitigation of an underperforming RAN device. In another aspect, thiscan enable reporting of improvements related to alterations to a RAN orRAN device. In a further aspect, access to performance outlierinformation can facilitate planning for entities associated with a RAN.

At 950, method 900 can comprise receiving performance profileinformation. Performance profile information can comprise informationrelated to a performance profile and can allow for outlier definitionprofiles that set conditions, that can be the same or different, bothfor the same or different metrics on one or more RAN devices, e.g., anoutlier can be defined for a first RAN device and a different value canbe defined for a second RAN device, while another outlier can be definedas a change in a value for both the first and second RAN device. Ofnote, more than one profile can be received, allowing for rapid changesbetween profiles, e.g., a test profile, a normal operation profile, amalware response profile, etc.

At 960, method 900 can comprise initiating an alert condition. At thispoint method 900 can end. The initiating the alert condition can be inresponse to performance outlier information being determined to satisfya rule related to the performance profile information. As an example,where performance outlier analysis indicates a measured level of activedata sessions satisfies a rule related to a corresponding valuecomprised in the performance profile information, an alarm can betriggered.

In an aspect, method 900 can be performed on a computing device oracross multiple computing devices, e.g., a distributed computing system.This can allow method 900 to effectively process related data in atimely and cost effective manner. Of note, this also allows method 900to scale. With sufficient scaling, the present disclosure can allow fornear real-time detection of performance outliers, acute offender RANdevices, etc.

FIG. 10 illustrates a method 1000 that facilitates determiningperformance outlier information via a distributed computing environmentin accordance with aspects of the subject disclosure. At 1010, method1000 can comprise receiving RAN data. RAN data can comprise currentand/or historical information related to a RAN device. In someembodiments, RAN data can comprise KPIs related to operation of a RANdevice. Historical RAN data can comprise historic information related toa RAN device and, in some embodiments, can comprise historical KPIs. Inan aspect, historical RAN data can comprise some, none, or all,historical RAN data for one or more RAN devices related to outlieranalysis to be performed. In some embodiments, historical RAN data cancomprise historical information related to a RAN determined to berelevant to an outlier analysis. Of note, historical RAN data can bereceived from nearly any source, e.g., a RAN device can store its ownhistorical information and provide access thereto when requested,historical RAN information can be compiled on carrier-side servers orother data stores, historical RAN information can be warehoused inthird-party data stores, historical RAN information can be stored acrossone or more data stores communicatively coupled to a device executingmethod 1000. Nearly any data associated with the operation of a RANdevice or the environment and characteristics of the RAN device orassociated network can be part of RAN data and can be current orhistorical in nature.

At 1020, method 1000 can comprise determining a performance outliercomputation task. The performance outlier computation task cancorrespond to a portion of an outlier analysis that can be processed ina distributed computing environment. In an aspect, a plurality ofperformance outlier computation tasks can be executed in a manner thatresults in determining an outlier condition based on the RAN datareceived at 1010. At 1030, method 1000 can comprise, delegating theperformance outlier computation task to a computing device of adistributed computing environment. This can allow method 1000 toeffectively process related data in a timely and cost effective manner.Of note, this also allows method 1000 to scale. With sufficient scaling,the present disclosure can allow for near real-time detection ofperformance outliers, acute offender RAN devices, etc.

At 1040, method 1000 can comprise, determining performance outlierinformation based on a result associated with computation of theperformance outlier computation task by the computing device of thedistributed competent environment. In an aspect, one or more computingdevice, e.g., in a distributed computing environment, can each processone or more performance outlier computation task(s), such as a remotelylocated computing device receiving the computing task, collectingrelevant historical RAN data, and determining a value that can bereturned as a result of the computation on the RAN data and thehistorical RAN data. At 1040, the returned values from the distributedcomputations can be used to determine performance outlier information.Performance outlier information can comprise information related toperformance aspects of a RAN device or associated network, includinginformation related to atypical or anomalistic behavior, e.g., aperformance outlier. A performance outlier can be related to RAN devicerelated values that are outside of a determined or specified normaloperation parameter. In an aspect, the instant disclosure can enabledetermination of normal behavior for a RAN device with regard tohistorically similar environments.

Method 1000 can include, at 1050, enabling access to the performanceoutlier information. Performance outlier information can be employed byother devices or methods to respond to, track, log, etc., a performanceoutlier for a RAN device. In an aspect, this can facilitate correctionor mitigation of an underperforming RAN device. In another aspect, thiscan enable reporting of improvements related to alterations to a RAN orRAN device. In a further aspect, access to performance outlierinformation can facilitate planning for entities associated with a RAN.

At 1060, method 1000 can comprise updating a response condition. At thispoint method 1000 can end. The updating the response condition can be inresponse to performance outlier information being determined to satisfya rule related to a trigger condition, such as can be accessed from aperformance profile.

FIG. 11 is a schematic block diagram of a computing environment 1100with which the disclosed subject matter can interact. The system 1100comprises one or more remote component(s) 1110. The remote component(s)1110 can be hardware and/or software (e.g., threads, processes,computing devices). In some embodiments, remote component(s) 1110 cancomprise servers, personal servers, wireless telecommunication networkdevices, etc. As an example, remote component(s) 1110 can be RAN devicesthat can be sources of RAN data 102-502, dispatching component 294,carrier planning component 299, POAC 522 and 524 relative to POAC 520,any distributed computing environment device, etc.

The system 1100 also comprises one or more local component(s) 1120. Thelocal component(s) 1120 can be hardware and/or software (e.g., threads,processes, computing devices). In some embodiments, local component(s)1120 can comprise, for example, POAC 120-520, etc.

One possible communication between a remote component(s) 1110 and alocal component(s) 1120 can be in the form of a data packet adapted tobe transmitted between two or more computer processes. Another possiblecommunication between a remote component(s) 1110 and a localcomponent(s) 1120 can be in the form of circuit-switched data adapted tobe transmitted between two or more computer processes in radio timeslots. The system 1100 comprises a communication framework 1140 that canbe employed to facilitate communications between the remote component(s)1110 and the local component(s) 1120, and can comprise an air interface,e.g., Uu interface of a UMTS network. Remote component(s) 1110 can beoperably connected to one or more remote data store(s) 1150, such as ahard drive, solid state drive, SIM card, device memory, etc., that canbe employed to store information on the remote component(s) 1110 side ofcommunication framework 1140. Similarly, local component(s) 1120 can beoperably connected to one or more local data store(s) 1130, that can beemployed to store information on the local component(s) 1120 side ofcommunication framework 1140.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 12, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that performs particulartasks and/or implement particular abstract data types.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It is noted that thememory components described herein can be either volatile memory ornonvolatile memory, or can comprise both volatile and nonvolatilememory, by way of illustration, and not limitation, volatile memory 1220(see below), non-volatile memory 1222 (see below), disk storage 1224(see below), and memory storage 1246 (see below). Further, nonvolatilememory can be included in read only memory, programmable read onlymemory, electrically programmable read only memory, electricallyerasable read only memory, or flash memory. Volatile memory can compriserandom access memory, which acts as external cache memory. By way ofillustration and not limitation, random access memory is available inmany forms such as synchronous random access memory, dynamic randomaccess memory, synchronous dynamic random access memory, double datarate synchronous dynamic random access memory, enhanced synchronousdynamic random access memory, Synchlink dynamic random access memory,and direct Rambus random access memory. Additionally, the disclosedmemory components of systems or methods herein are intended to comprise,without being limited to comprising, these and any other suitable typesof memory.

Moreover, it is noted that the disclosed subject matter can be practicedwith other computer system configurations, comprising single-processoror multiprocessor computer systems, mini-computing devices, mainframecomputers, as well as personal computers, hand-held computing devices(e.g., personal digital assistant, phone, watch, tablet computers,netbook computers, . . . ), microprocessor-based or programmableconsumer or industrial electronics, and the like. The illustratedaspects can also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network; however, some if not all aspects ofthe subject disclosure can be practiced on stand-alone computers. In adistributed computing environment, program modules can be located inboth local and remote memory storage devices.

FIG. 12 illustrates a block diagram of a computing system 1200 operableto execute the disclosed systems and methods in accordance with anembodiment. Computer 1212, which can be, for example, POAC 120-520, 522,524, etc., carrier planning component 299, dispatching component 294,mobile device 220, RAN device 222, etc., comprises a processing unit1214, a system memory 1216, and a system bus 1218. System bus 1218couples system components comprising, but not limited to, system memory1216 to processing unit 1214. Processing unit 1214 can be any of variousavailable processors. Dual microprocessors and other multiprocessorarchitectures also can be employed as processing unit 1214.

System bus 1218 can be any of several types of bus structure(s)comprising a memory bus or a memory controller, a peripheral bus or anexternal bus, and/or a local bus using any variety of available busarchitectures comprising, but not limited to, industrial standardarchitecture, micro-channel architecture, extended industrial standardarchitecture, intelligent drive electronics, video electronics standardsassociation local bus, peripheral component interconnect, card bus,universal serial bus, advanced graphics port, personal computer memorycard international association bus, Firewire (Institute of Electricaland Electronics Engineers 1194), and small computer systems interface.

System memory 1216 can comprise volatile memory 1220 and nonvolatilememory 1222. A basic input/output system, containing routines totransfer information between elements within computer 1212, such asduring start-up, can be stored in nonvolatile memory 1222. By way ofillustration, and not limitation, nonvolatile memory 1222 can compriseread only memory, programmable read only memory, electricallyprogrammable read only memory, electrically erasable read only memory,or flash memory. Volatile memory 1220 comprises read only memory, whichacts as external cache memory. By way of illustration and notlimitation, read only memory is available in many forms such assynchronous random access memory, dynamic read only memory, synchronousdynamic read only memory, double data rate synchronous dynamic read onlymemory, enhanced synchronous dynamic read only memory, Synchlink dynamicread only memory, Rambus direct read only memory, direct Rambus dynamicread only memory, and Rambus dynamic read only memory.

Computer 1212 can also comprise removable/non-removable,volatile/non-volatile computer storage media. FIG. 12 illustrates, forexample, disk storage 1224. Disk storage 1224 comprises, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, flash memory card, or memory stick. In addition, disk storage1224 can comprise storage media separately or in combination with otherstorage media comprising, but not limited to, an optical disk drive suchas a compact disk read only memory device, compact disk recordabledrive, compact disk rewritable drive or a digital versatile disk readonly memory. To facilitate connection of the disk storage devices 1224to system bus 1218, a removable or non-removable interface is typicallyused, such as interface 1226.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media or communications media, whichtwo terms are used herein differently from one another as follows.

Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media cancomprise, but are not limited to, read only memory, programmable readonly memory, electrically programmable read only memory, electricallyerasable read only memory, flash memory or other memory technology,compact disk read only memory, digital versatile disk or other opticaldisk storage, magnetic cassettes, magnetic tape, magnetic disk storageor other magnetic storage devices, or other tangible media which can beused to store desired information. In this regard, the term “tangible”herein as may be applied to storage, memory or computer-readable media,is to be understood to exclude only propagating intangible signals perse as a modifier and does not relinquish coverage of all standardstorage, memory or computer-readable media that are not only propagatingintangible signals per se. In an aspect, tangible media can comprisenon-transitory media wherein the term “non-transitory” herein as may beapplied to storage, memory or computer-readable media, is to beunderstood to exclude only propagating transitory signals per se as amodifier and does not relinquish coverage of all standard storage,memory or computer-readable media that are not only propagatingtransitory signals per se. Computer-readable storage media can beaccessed by one or more local or remote computing devices, e.g., viaaccess requests, queries or other data retrieval protocols, for avariety of operations with respect to the information stored by themedium. As such, for example, a computer-readable medium can compriseexecutable instructions stored thereon that, in response to execution,cause a system comprising a processor to perform operations, comprising:receiving trigger information a remote device, e.g., a UE, and inresponse, generating communication augmentation information that can beaccessed via an air interface or other wireless interface by one or moreservice interface components or other UEs to enable context sensitivecommunication augmentation.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

It can be noted that FIG. 12 describes software that acts as anintermediary between users and computer resources described in suitableoperating environment 1200. Such software comprises an operating system1228. Operating system 1228, which can be stored on disk storage 1224,acts to control and allocate resources of computer system 1212. Systemapplications 1230 take advantage of the management of resources byoperating system 1228 through program modules 1232 and program data 1234stored either in system memory 1216 or on disk storage 1224. It is to benoted that the disclosed subject matter can be implemented with variousoperating systems or combinations of operating systems.

A user can enter commands or information into computer 1212 throughinput device(s) 1236. In some embodiments, a user interface can allowentry of user preference information, etc., and can be embodied in atouch sensitive display panel, a mouse input GUI, a command linecontrolled interface, etc., allowing a user to interact with computer1212. As an example, carrier panning component 299, performance profilecomponent 360, 460, etc., POAC 120-524, etc., can receive touch, motion,audio, visual, or other types of input. Input devices 1236 comprise, butare not limited to, a pointing device such as a mouse, trackball,stylus, touch pad, keyboard, microphone, joystick, game pad, satellitedish, scanner, TV tuner card, digital camera, digital video camera, webcamera, cell phone, smartphone, tablet computer, etc. These and otherinput devices connect to processing unit 1214 through system bus 1218 byway of interface port(s) 1238. Interface port(s) 1238 comprise, forexample, a serial port, a parallel port, a game port, a universal serialbus, an infrared port, a Bluetooth port, an IP port, or a logical portassociated with a wireless service, etc. Output device(s) 1240 use someof the same type of ports as input device(s) 1236.

Thus, for example, a universal serial busport can be used to provideinput to computer 1212 and to output information from computer 1212 toan output device 1240. Output adapter 1242 is provided to illustratethat there are some output devices 1240 like monitors, speakers, andprinters, among other output devices 1240, which use special adapters.Output adapters 1242 comprise, by way of illustration and notlimitation, video and sound cards that provide means of connectionbetween output device 1240 and system bus 1218. It should be noted thatother devices and/or systems of devices provide both input and outputcapabilities such as remote computer(s) 1244.

Computer 1212 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1244. Remote computer(s) 1244 can be a personal computer, a server, arouter, a network PC, cloud storage, a cloud service, code executing ina cloud-computing environment, a workstation, a microprocessor basedappliance, a peer device, or other common network node and the like, andtypically comprises many or all of the elements described relative tocomputer 1212.

For purposes of brevity, only a memory storage device 1246 isillustrated with remote computer(s) 1244. Remote computer(s) 1244 islogically connected to computer 1212 through a network interface 1248and then physically connected by way of communication connection 1250.Network interface 1248 encompasses wire and/or wireless communicationnetworks such as local area networks and wide area networks. Local areanetwork technologies comprise fiber distributed data interface, copperdistributed data interface, Ethernet, Token Ring and the like. Wide areanetwork technologies comprise, but are not limited to, point-to-pointlinks, circuit-switching networks like integrated services digitalnetworks and variations thereon, packet switching networks, and digitalsubscriber lines. As noted below, wireless technologies may be used inaddition to or in place of the foregoing.

Communication connection(s) 1250 refer(s) to hardware/software employedto connect network interface 1248 to bus 1218. While communicationconnection 1250 is shown for illustrative clarity inside computer 1212,it can also be external to computer 1212. The hardware/software forconnection to network interface 1248 can comprise, for example, internaland external technologies such as modems, comprising regular telephonegrade modems, cable modems and digital subscriber line modems,integrated services digital network adapters, and Ethernet cards.

The above description of illustrated embodiments of the subjectdisclosure, comprising what is described in the Abstract, is notintended to be exhaustive or to limit the disclosed embodiments to theprecise forms disclosed. While specific embodiments and examples aredescribed herein for illustrative purposes, various modifications arepossible that are considered within the scope of such embodiments andexamples, as those skilled in the relevant art can recognize.

In this regard, while the disclosed subject matter has been described inconnection with various embodiments and corresponding Figures, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

As it employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to comprising, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit, a digital signalprocessor, a field programmable gate array, a programmable logiccontroller, a complex programmable logic device, a discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. Processorscan exploit nano-scale architectures such as, but not limited to,molecular and quantum-dot based transistors, switches and gates, inorder to optimize space usage or enhance performance of user equipment.A processor may also be implemented as a combination of computingprocessing units.

As used in this application, the terms “component,” “system,”“platform,” “layer,” “selector,” “interface,” and the like are intendedto refer to a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration and not limitation, both anapplication running on a server and the server can be a component. Oneor more components may reside within a process and/or thread ofexecution and a component may be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. The components may communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). Asanother example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry, which is operated by a software or firmwareapplication executed by a processor, wherein the processor can beinternal or external to the apparatus and executes at least a part ofthe software or firmware application. As yet another example, acomponent can be an apparatus that provides specific functionalitythrough electronic components without mechanical parts, the electroniccomponents can comprise a processor therein to execute software orfirmware that confers at least in part the functionality of theelectronic components.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

Further, the term “include” is intended to be employed as an open orinclusive term, rather than a closed or exclusive term. The term“include” can be substituted with the term “comprising” and is to betreated with similar scope, unless otherwise explicitly used otherwise.As an example, “a basket of fruit including an apple” is to be treatedwith the same breadth of scope as, “a basket of fruit comprising anapple.”

Moreover, terms like “user equipment (UE),” “mobile station,” “mobile,”subscriber station,” “subscriber equipment,” “access terminal,”“terminal,” “handset,” and similar terminology, refer to a wirelessdevice utilized by a subscriber or user of a wireless communicationservice to receive or convey data, control, voice, video, sound, gaming,or substantially any data-stream or signaling-stream. The foregoingterms are utilized interchangeably in the subject specification andrelated drawings. Likewise, the terms “access point,” “base station,”“Node B,” “evolved Node B,” “eNodeB,” “home Node B,” “home accesspoint,” and the like, are utilized interchangeably in the subjectapplication, and refer to a wireless network component or appliance thatserves and receives data, control, voice, video, sound, gaming, orsubstantially any data-stream or signaling-stream to and from a set ofsubscriber stations or provider enabled devices. Data and signalingstreams can comprise packetized or frame-based flows.

Additionally, the terms “core-network”, “core”, “core carrier network”,“carrier-side”, or similar terms can refer to components of atelecommunications network that typically provides some or all ofaggregation, authentication, call control and switching, charging,service invocation, or gateways. Aggregation can refer to the highestlevel of aggregation in a service provider network wherein the nextlevel in the hierarchy under the core nodes is the distribution networksand then the edge networks. UEs do not normally connect directly to thecore networks of a large service provider but can be routed to the coreby way of a switch or radio access network. Authentication can refer todeterminations regarding whether the user requesting a service from thetelecom network is authorized to do so within this network or not. Callcontrol and switching can refer determinations related to the futurecourse of a call stream across carrier equipment based on the callsignal processing. Charging can be related to the collation andprocessing of charging data generated by various network nodes. Twocommon types of charging mechanisms found in present day networks can beprepaid charging and postpaid charging. Service invocation can occurbased on some explicit action (e.g. call transfer) or implicitly (e.g.,call waiting). It is to be noted that service “execution” may or may notbe a core network functionality as third party network/nodes may takepart in actual service execution. A gateway can be present in the corenetwork to access other networks. Gateway functionality can be dependenton the type of the interface with another network.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer,”“prosumer,” “agent,” and the like are employed interchangeablythroughout the subject specification, unless context warrants particulardistinction(s) among the terms. It should be appreciated that such termscan refer to human entities or automated components (e.g., supportedthrough artificial intelligence, as through a capacity to makeinferences based on complex mathematical formalisms), that can providesimulated vision, sound recognition and so forth.

Aspects, features, or advantages of the subject matter can be exploitedin substantially any, or any, wired, broadcast, wirelesstelecommunication, radio technology or network, or combinations thereof.Non-limiting examples of such technologies or networks comprisebroadcast technologies (e.g., sub-Hertz, extremely low frequency, verylow frequency, low frequency, medium frequency, high frequency, veryhigh frequency, ultra-high frequency, super-high frequency, terahertzbroadcasts, etc.); Ethernet; X.25; powerline-type networking, e.g.,Powerline audio video Ethernet, etc.; femtocell technology; Wi-Fi;worldwide interoperability for microwave access; enhanced general packetradio service; third generation partnership project, long termevolution; third generation partnership project universal mobiletelecommunications system; third generation partnership project 2, ultramobile broadband; high speed packet access; high speed downlink packetaccess; high speed uplink packet access; enhanced data rates for globalsystem for mobile communication evolution radio access network;universal mobile telecommunications system terrestrial radio accessnetwork; or long term evolution advanced.

What has been described above includes examples of systems and methodsillustrative of the disclosed subject matter. It is, of course, notpossible to describe every combination of components or methods herein.One of ordinary skill in the art may recognize that many furthercombinations and permutations of the claimed subject matter arepossible. Furthermore, to the extent that the terms “includes,” “has,”“possesses,” and the like are used in the detailed description, claims,appendices and drawings such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

What is claimed is:
 1. A device, comprising: a processor; and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: determining a performance outlier value of historical performance indicator values, wherein the historical performance indicator values relate to performance of a radio access network device of a mobile communication network, wherein the determining the performance outlier value comprises determining a correlation coefficient via a binary array embodying the historical performance indicator values, and wherein a historical performance indicator value of the historical performance indicator values corresponds to an event affecting the mobile communication network; and causing adaptation of the mobile communication network based on access to information related to the performance outlier value.
 2. The device of claim 1, wherein the information related to the performance outlier value comprises the performance outlier value.
 3. The device of claim 1, wherein the determining the performance outlier value is based on a Pearson Product Moment Correlation function.
 4. The device of claim 1, wherein the historical performance indicator value reflects a characteristic of an operating environment of the radio access network device.
 5. The device of claim 4, wherein the characteristic is a temporal characteristic pertaining to a time of operation of the operating environment.
 6. The device of claim 4, wherein the characteristic is an event characteristic pertaining to the event, and wherein the event is determined to have occurred in the operating environment.
 7. The device of claim 4, wherein the characteristic is a software characteristic of the radio access network device.
 8. The device of claim 4, wherein the characteristic is a hardware characteristic of the radio access network device.
 9. The device of claim 1, wherein the adaptation of the mobile communication network comprises initiating a response based on the performance outlier value.
 10. The device of claim 9, wherein the response is a repair response to correct a condition of the mobile communications network corresponding to the performance outlier value.
 11. The device of claim 10, wherein the repair response is associated with repair of hardware of the radio access network device.
 12. The device of claim 10, wherein the repair response is associated with alteration of software of the radio access network device.
 13. The device of claim 9, wherein the response is a planning response associated with modifying a future deployment of radio access network devices of the mobile communication network.
 14. A method, comprising: in response to determining a strength of a linear relationship between historical performance indicator values in a binary array satisfy a rule related to a threshold strength, determining, by a system comprising a processor, outlier information related to a performance outlier event, wherein the performance outlier event is embodied in the historical performance indicator values, and wherein the historical performance indicator values reflect a performance of a mobile communication network comprising a radio access network device; and initiating, by the system, a response event based on the outlier information, resulting in altering a future performance of the mobile communication network.
 15. The method of claim 14, wherein the determining the outlier information employs a Pearson Product Moment Correlation function.
 16. The method of claim 14, wherein the initiating the response event comprises causing altering of software of the radio access network device.
 17. The method of claim 14, wherein the initiating the response event comprises causing altering of hardware of the radio access network device.
 18. The method of claim 14, wherein the initiating the response event comprises causing altering a future deployment of radio access network devices of the mobile communication network.
 19. A machine-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising: determining a strength of a linear relationship between historical performance indicator values in a binary array, wherein the historical performance indicator values correspond to a performance of radio access network devices of a mobile communication network; and in response to determining that the strength of the linear relationship satisfies a rule related to a threshold strength, initiating an action that results in altering a future performance of the mobile communication network, wherein the initiating the action is based on outlier information related to a performance outlier event, and wherein the performance outlier event is reflected in the historical performance indicator values.
 20. The machine-readable storage medium of claim 19, wherein the determining the strength of the linear relationship between historical performance indicator values in the binary array is via a Pearson Product Moment Correlation function. 